Events and event spaces

An understanding of sets is crucial for dealing with events and the basic rules of probability: this includes set notation, the subset relationship, and operations on sets such as union and intersection.

The event space \(\mathcal{E}\) is the set of the possible distinct outcomes of the random process. The event space is sometimes called the sample space. When rolling a normal six-sided die and recording the uppermost face, the event space is \(\mathcal{E} = \{1,2,3,4,5,6\}\).

An event is a collection of possible outcomes, and therefore it is a subset of \(\mathcal{E}\). The event \(A\) occurs if the observed outcome is in \(A\), and does not occur if the observed outcome is not in \(A\). It is usual to denote an event by a capital letter, commonly near the start of the alphabet: \(A, B, C, \dots\).

An event space ε depicted as a region enclosed in an elliptical type loop and three events A, B and C
The event space.
Detailed description

Events may be expressed in words, inside quotation marks:

When expressing an event in words like this, the description must determine a subset of \(\mathcal{E}\). We can regard something as an event only when it is true that, for every possible outcome, we can say definitively whether or not the event has occurred.

An outcome is sometimes called an elementary event.

The following are events in any context:

The mathematical approach to probability requires that we have to be able to specify the possible outcomes. Outcomes that we may be able to imagine, such as the die coming to rest on a corner (leading to no number being uppermost), are often excluded from consideration altogether. If we do want to consider an outcome as possible, it must be included in the event space.

Recall that the domain of probability statements consists of events. Now that we understand that events are subsets of the event space \(\mathcal{E}\), we see that probability is a set function. It maps subsets of \(\mathcal{E}\) into the interval \([0,1]\).

Probability as a set function.
Detailed description

Operations on events and relations between events

Events in random procedures are sets of possible outcomes. We now apply ideas about sets to events; this will help us to think clearly about the events and, subsequently, their probabilities.

For a subset \(A\) of the event space \(\mathcal{E}\), the complementary set is

\[ A' = \{\, x \in \mathcal{E} : x \notin A \,\}. \]

In words, \(A'\) is the set of all elements of the universal set \(\mathcal{E}\) that are not in \(A\).

For the event \(A\), the complementary event is \(A'\). For example:

We are interested in relations between events, and therefore in considering more than one event.

For two events \(A\) and \(B\), the intersection \(A \cap B\) is the event ``quot;\(A\) and \(B\) both occur". In the die-rolling example, if \(A\) = "a prime number is obtained" and \(B\) = "an even number is obtained", then \(A = \{2,3,5\}\) and \(B = \{2,4,6\}\), so \(A \cap B = \{2\}\). As is the case with sets generally, we can consider the intersection of more than two events: the event \(A \cap B \cap C\) is the event ``quot;\(A\) and \(B\) and \(C\) all occur", and so on.

For two events \(A\) and \(B\), the union \(A \cup B\) is the event ``quot;either \(A\) or \(B\) occurs, or both". Equivalently, the union \(A \cup B\) is the event ``quot;at least one of \(A\) and \(B\) occurs". For the die-rolling example again, with \(A = \{2,3,5\}\) and \(B = \{2,4,6\}\), the union is \(A \cup B = \{2,3,4,5,6\}\). We may be interested in the union of more than two events, such as \(A \cup B \cup C\).

We say that an event \(A\) is a subset of an event \(B\), and write \(A \subseteq B\), when all outcomes in \(A\) are also in \(B\). For example, suppose two dice are rolled. If \(A\) = "the sum of the two numbers obtained is 12" and \(B\) = "a six is obtained on the first die", then \(A = \{(6,6)\}\) and \(B = \{(6,1), (6,2), (6,3), (6,4), (6,5), (6,6)\}\), so \(A \subseteq B\).

We say that two events \(A\) and \(B\) are mutually exclusive if their intersection is the empty set, which means they have no outcomes in common. For example, an event \(A\) and its complementary event \(A'\) are mutually exclusive, since \(A'\) consists of all outcomes that are not in \(A\). In the example of five people born in 1995, if we define \(X\) to be the number of individuals experiencing a broken leg prior to turning 20, then the event ``quot;\(X = 1\)" and the event ``quot;\(X = 0\)" are mutually exclusive: they cannot both occur.

Exercise 1

Consider rolling two normal dice and observing the uppermost faces obtained.

  1. For this random procedure, which of the following are events?
    1. \(A\) = "the sum of the two numbers observed is a prime number"
    2. \(B\) = "Barry sneezes just before the dice are rolled"
    3. \(C\) = "at least one of the two dice shows an odd number"
    4. \(D\) = "at least one of the two dice shows a six"
    5. \(E\) = "the outcome \((6,6)\) is observed"
  2. Which outcomes are in the event \(A\)?
  3. How many outcomes are in \(D\)?
  4. Describe \(A \cap E\) in words.

Exercise 2

Observations are made by the Bureau of Meteorology on a city's weather every day, in a systematic fashion. Suppose that we define a random procedure to be: record the daily amounts of rainfall, to the nearest tenth of a millimetre, in August 2017.

  1. For this random procedure, which of the following are events?
    1. \(A\) = "the only days with recorded rainfall greater than 0.0 mm are Mondays"
    2. \(B\) = "the main street floods on Tuesday 15 August"
    3. \(C\) = "the total rainfall for the month is greater than 20.0 mm"
  2. Consider the following events:

Which of the following pairs of events are mutually exclusive?
  1. \(A\) and \(E\)
  2. \(A\) and \(F\)
  3. \(D\) and \(E\)
  4. \(E\) and \(F\)
  1. Describe the following events in words:
    1. \(A'\)
    2. \(A \cap E\)
    3. \(A \cup C\).

Next page - Content - The probability axioms